OVERVIEW upon consumers require- ments. Particle swarm optimization (PSO)

OVERVIEW OF THE PROJECTCloud  computing isa model  for enabling ubiquitous, convenient, on-demand net-work  accessto a shared pool of configurable computing resources that can be rapidlyprovisioned and  released with  service  provider interaction .

It is a new paradigm fordelivering on-demand resources for customers through internet.A service is a mech- anism that is capable of providing one  or more functionalities,which  it is possibleto use in compliance with provider definedrestrictions and  rules  and  through an in- terface .There  are three  services  models in cloud.  Theyare Software  as a service: A software or application that is executing on a vendors infrastructure isrecognized as a service providedthat the consumer has limited permission to accessand the provisionis through a thin clientor a program interface for sending data  andreceiving results.  The consumer isunaware ofthe application providersinfrastructure and has lim- ited authority to configure some settings. Platformas a service:  In this servicesmodel, the servicevendor provides moderatebasic requisites, includingthe operating system, network and servers, and development tools to allow  the consumer to develop ac-quired applications or software and manage their configurable settings. Infrastructureas a service: The cloud service consumer has developed the required applications and needs  only a basic infrastructure. Insuch cases, processors,networks, and storage canbe provided byvendors as services with consumer provisions.

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Cloud  service Ranking is needed tocloud service consumers to choose appropriate cloud service from a pool of available cloud  services.   The Qos parameters such as response time,  availability, throughput,etc.  are used  to rank the cloud  services  based  upon consumers require-ments.  Particle  swarm optimization (PSO) is a computational method that optimizes aproblem by iteratively trying  to improve a candidate solutionwith regard to a given measure of quality. PSO optimizes aproblem by having a population of candidate so- lutions, here dubbed particles, and moving these particles around in the search-space according to simple  mathematical formulae.                                                                                           1.2 PROBLEM STATEMENT            In cloudservice ranking approach only few quantifiable parameters QoS attributes wereused for ranking.

Several non-quantifiable QoS attributes have major impact inthe ranking and selection process. Also, static ranking of cloud services mayprovide inappropriate cloud service to cloud service consumers as therequirements of one consumer vary with another. The dynamic ranking andselection of cloud services is solved by designing a cloud broker model withseveral components work together to perform Cloud  Service Ranking and Selection using  Particle Swarm  Optimization. 1.3 CHALLENGES AND SCOPE·        The accuracy achieved through thisproject is 94% which can be increased further.·        The classifiers considered can bechanged further to improve efficiency.·        The proposed project is subject to textmining and so still other mining techniques like spatial and correlationtechniques can be used.

        CHAPTER2LITERATURE SURVEY2.1 REVIEW      In this paper, they survey state-of-the-art Cloud  services  selection  approaches, which are analyzed from the following five perspectives: decision-making techniques; data representation models;   parameters and characteristics of Cloud  services;  contexts, purposes.  After comparing and summarizing the  approaches they identify the  pri- mary research issues  in Cloud  service selection.   Optimization-basedapproaches for Cloud  service selection:  Chang  etal.(2012) developed adynamic programming-basedalgorithm to select Cloud  storage providers that can maximize the data survival prob- abilityor the amount of surviving data, subjectto a fixed budget. They formulated the problem of multiple storageservice  provider selection into a probability model  with clearly defined object functions and cost measurements. The availability of the storage service is quantitatively analyzed bytwo methods minimum failure probability with a given budget, and maximum validitywith a given budget. Sundareswaranetal.

(2012) employed a greedy algorithm- based  method for Cloud  service selection.   They pro- posed the use of the B+ tree to index Cloud service providers (i.e.CSPindexing) and encode  services and user requirements. The indexing structure supports the  indexing of serviceproperties andthe modeling of their relative  importance, as ordered by users.  It enables  fastinformation retrieval for decision  makers. Martensand Teuteberg(2012) developed a scalable mathematical decision  model for discrete dynamic opti- mization problems in Cloud  service selection.

 The model helps organizations to iden- tifysuitable Cloud  services by minimizing costs and risks. An AHP-based approach isproposed tomeasure the relative  importance of the services in a business process  and the  relative importance of security parameters in a risk evaluation process.  Finally, decisions can be made  by solving  the  formulated mathematical models.  Optimiza- tion techniques, such as linear,  non-linear,  and genetic  algorithms, are recommended as the  tools  for solving models, depending on the  specific  service  outsourcing sce-narios.   Identified issues:  The open  issues  on contemporary Cloud  service selection approaches are 1.  Lack of a marketplacefor Cloud  service publication and  transac- tion:  Cloud  services  do not have  a standard for service  publicationand  registry. Thelack of detailed service  QoS information makes  it difficult  for service  users  to make educated purchasingdecisions.

Cloud  service allows service users to rate and  make comments on services,  but there  is no feedback from users. 2. Lack of normalizationfor Cloud servicedescription serving different kinds of users:  The flourishing of Cloud services highlights the need for a unified specification for Cloud  services.  A high level of abstraction and  support forthe simple  publication, discovery, selection, and use of resources for both service  providers and users is needed.

3. Lack of a search engine system for the automatic identification and updating of Cloud  service  information: Cloud  service  specification lacks a standard form,  especially for IaaS and  PaaS. The service  information is typically published as plain  text on a Web page,  which  usuallynarrows to a functional description rather than being complete enough to include tech- nical details. Such incompleteness prevents keyword-based search  engines returning accurate services.

 4. Lack of an efficientmeans to deal with qualitative parameters and fuzzy expression:Qualitative non-functional properties such as security and availabil-ity increase  the fuzziness of service  evaluation. Current techniques are more focused on quantitative criteria  that  can  be measured via precise  numerical values such as response time, storage space and  network latency.  Hence  an efficientmethod of han- dling  uncertainty and  fuzziness in service  specification and  user  requirements needs to be taken  into  account forthe  chosen  services.  5.

 Lessconcern  on multi-tenancy service  selection.   6.  Lackof an advanced multi-criteria-based measurement ofuser preferences. 7. Lack of consideration of the interdependency ofcriteria  8.Lack of long term performance predication and dynamic application strategy.           CHAPTER 3DESIGN AND IMPLEMENTATION3.1 EXISTING SYSTEMThe data mining technique that is beingused comprise of a model that helps in training the train data set.

The modelis made up of techniques without any Cross Validations and repeats. Hence theobtained accuracy is around 92%. The false positive rate is also high. Thoughall kind of vulnerabilities are considered, the results of all vulnerabilitiesare of the same accuracy. The vulnerabilities include XSS, SQL Injection.

   3.2 PROPOSED SYSTEM             Almostall web applications is moving from a traditional deployment strategy to anon-demand cloud environment. It is highly difficult for the cloud serviceconsumers to  choose wisely between theavailable cloud providers. On the other hand, each and every cloud provider mayhave interest on different parameters to be set for their infrastructures.Also, there is no common registry to register the service level agreement ofcloud service as that of the web services.

Hence, it becomes difficult for theconsumers to choose appropriately the required services and thereby cloudservice providers.  CHAPTER 4DESIGN AND IMPLEMENTATION4.1 OVERALL DESCRIPTIONThe proposed cloud brokerarchitecture has three  components.

Theyare Cloud  Service Consumer; theindividual or an organization that requires a cloud service either to deploy anapplication or for application development, Cloud  Broker; is the middleware that receives inputfrom the cloud consumers as well as the cloud service providers. It checks theservice level objectives with that of the service level agreement and makes thedecision processing to rank and thereby select the cloud service. Cloud  Service Provider; is an entity that providescloud services to the end users or cloud service consumers.   4.2 ARCHITECTURE DIAGRAM          The CloudBroker has two databases SLA repository andQos information reposi- tory and has probation manager, rank manager, co-ordinationAgent and search agent. The SLAs of cloud service providersare stored in the SLA Repository of cloud broker.

The SLA document consists  of the quantifiable and non- quantifiable Qos parameterswhich  include service  name,  cloud provider, security, availability, processor speed,cost per hour, storage, bandwidth, performance, etc.,             The Probation Manager :  takes SLA from SLA repository and  checks the parameters of SLA during the probation period. After the validation it informs the rank  managerwith updated parameters.

             The Rank Manager: hasrank  table and  updates therank  table with  SLA parameter given by probation manager. Rank table contains ranking ofcloud  services  according to the SLA parameters. If a service  is longer used by a consumer, then  rank  manager gives the service to probationmanager for validation.The Qosinformation repository: feedback of the past customer experienceare storedin Qos Information Repository.

 4.3 LIST OF MODULES1. Build SLARepositoryand Design Cloud Broker        – Probation Manager- Rank Manager2. Build Qos InformationRepository & add into Cloud  Broker- Co-ordination Agent- Search AgentIntegration of cloud service consumers requirements with brokerCloud  Service Ranking and Selection using  PSO      4.

3.1SLA RepositoryStep1:         The SLA from  cloud  service  providers fordifferenet cloud services was  col-lected. The SLA document consistsof the quantifiable Qos parametersuca  as servicename,   Figure 2: Input SLA  cloud provider, security, availability, processorspeed, cost per hour, storage and non Qosparameter asbandwidth etc.

The SLA parameters are collected and  stored in Mysql server.                4.3.2Design of Cloud  BrokerThe cloud brokerhas four entities Probation Manager,Rank Manager ,Co-ordination Agent,Search Agent.

Using cloudsim, the broker  is created with  entities  along with cloudlets.                           4.3.3Probation  ManagerStep1:Simulation of Probation Manager  Figure 5: ProbationManager simulation  Step2:       TheProbation Managergets  the  SLAparameters from  the  database andpopulate the table with SLA.  Figure 6: ProbationManager gets SLA’s       4.

3.4  Timeline                                                    CHAPTER 5DEVELOPMENT ENVIRONMENT5.1 HARDWARE REQUIREMENTS            HARDWARE      CONFIGURATION       RAM               1 GB and above      Processor       Dual core and above      Hard Disk       80 GB and above Table:4.1hardware requirements 5.2 SOFTWARE REQUIREMENTS       SOFTWARE     VERSIONS     Operating System    Windows 7     Application Environment     Java(JDK)     Programming Language     Python  Table:4.2software requirements   CHAPTER 6CONCLUSION AND FUTURE WORKThedata thus has been filtered to figure out what are the data that are vulnerableand non-vulnerable data. The improved accuracy helps in better filtering ofdata.

The future work is to implement Ensembling models in order to achievestill better accuracy results. Also the method of preventing the vulnerabledata can also be proposed thereby preventing the impact of vulnerable dataduring the transmission of it and safeguarding the entire system.Ensemblingis a general term for combining many classifiers by averaging or voting.

It isa form of meta learning in that it focuses on how to merge results of arbitraryunderlying classifiers. Generally, ensembles of classifiers perform better thansingle classifiers, and the averaging process allows for more granularity ofchoice in the bias-variance tradeoff.Namesof ensemble techniques include bagging, boosting, modelaveraging, and weak learner theory.An obvious strategy isthus to implement as many different solvers as possible and ensemble them alltogether, a sort of “More Models are Better” approach.Text Mining is the keyto determine the vulnerable data at the source and efficient methods inadopting text mining will improve the mining results.         CHAPTER 7OUTPUT OF MODULES        CHAPTER 8 REFERENCES1. Buyyaet al., ?Cloud Computing and Emerging IT Platforms: Vision, Hype, and Realityfor Delivering Computing as the 5th Utility,Future Generation Computer Systems,vol.

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Karunasekera, ?Automaticmeasurement of a QoS metric for Web service recommendation,? in ProceedingsAustralian Software Engineering Conference, 2005, pp. 202–211. 4. J.Marden, Analyzing and Modeling Ranking Data. Chapman & Hall, 1995. 5.

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